چكيده به لاتين
In deterministic optimization models, the input data of the model are deterministic and equivalent to nominal values. this attitude does not consider the impact of uncertainty on optimality and feasibility of the model. The main disadvantage of deterministic models is their inability to respond to the uncertainty of real-world systems. In fact, data that take values that are different from their nominal values may lead to a number of limitations being violated, the optimal answer not being optimized in the long run, or even losing its validity and being infeasible. Robust optimization refers to the modeling of optimization problems in the face of data uncertainty, and the goal is to arrive at an answer that is feasible in the presence of all or most of the uncertain parameters. In this study, we have dealt with the issue of integrated train formation and shipment path. In this case, the purpose is to route and group wagons containing cargoes of demand, as well as to limitation of the capacity of railways and stations. Considering the importance of controlling the uncertainty of input parameters in railway issues and various applications in rail transportation planning in this research, by using the technique of robust budget optimization (Bertsimas and Sim), we provide robust models for the issue of train formation and shipment path. The results were obtained using CPLEX solver in GAMS software. The results of solving this model show that this model is able to achieve a high-quality answer. In this dissertation, the presented models are implemented on RAS competition (2019) data
